Self-Hosted Uptime Monitoring: Uptime Kuma, Gatus, and Statping-ng Compared

Paying $20-100/month for uptime monitoring (UptimeRobot Pro, Pingdom, Better Uptime) makes sense when you have one or two sites and no time to manage infrastructure. But once you're monitoring 10+ endpoints — or you want monitoring that stays within your own network — self-hosted alternatives start looking very attractive.

Photo by Michael Dziedzic on Unsplash

Here's a comparison of the three most popular self-hosted uptime monitoring tools, with honest assessments of each.

Why Self-Host Your Monitoring?

Before comparing tools, let's be clear about the trade-offs:

Self-hosted monitoring makes sense when:

• You're monitoring internal services that external tools can't reach
• You're monitoring many endpoints and SaaS pricing scales poorly
• You need custom check types (database queries, DNS, specific response validation)
• You want full control over alert routing and data retention

External monitoring makes sense when:

• You need to verify your site is reachable from outside your network
• You have 1-5 sites and don't want to manage another service
• Your monitoring infrastructure being on the same server as your apps defeats the purpose

The best approach is often both: a self-hosted tool for internal checks and a free tier of an external service (UptimeRobot free gives you 50 monitors) for outside-in verification.

Uptime Kuma

The popular choice. Uptime Kuma is by far the most widely used self-hosted monitoring tool, and for good reason — it's well-designed, feature-rich, and easy to set up.

What you get

• Web UI for managing monitors and viewing history
• Status pages — public status pages you can point a domain at
• 20+ notification channels — Slack, Discord, Telegram, email, webhook, PagerDuty, and more
• Monitor types — HTTP(S), TCP, DNS, Docker container, Steam game server, MQTT, and more
• Multi-language — translated into 30+ languages
• Two-factor auth and proxy support

Setup

docker run -d \
  --name uptime-kuma \
  -p 3001:3001 \
  -v uptime-kuma:/app/data \
  louislam/uptime-kuma:1

That's it. Visit http://your-server:3001, create an account, and start adding monitors. The entire setup takes under 5 minutes.

Strengths

• Excellent UI — The dashboard is clean, responsive, and well-organized
• Easy setup — Single Docker container, no database to configure (uses SQLite)
• Active community — 60k+ GitHub stars, regular updates, fast bug fixes
• Status pages — Built-in public status pages with incident management
• Certificate monitoring — Alerts before SSL certificates expire
• Maintenance windows — Schedule downtime without triggering alerts

Weaknesses

• Node.js + SQLite — Can struggle with 500+ monitors or long retention periods
• Single instance — No built-in clustering or HA (there are community workarounds)
• No API-first design — The API exists but is secondary to the web UI
• Browser-based only — No CLI for automation
• WebSocket-dependent — The UI uses WebSockets heavily, which can be tricky behind some reverse proxies

Resource requirements

• Minimum: 256 MB RAM, 1 vCPU
• Recommended: 512 MB RAM for 50+ monitors
• Storage: ~100 MB per year for 50 monitors at 60-second intervals

Gatus

The developer's choice. Gatus takes a fundamentally different approach: configuration-as-code. Everything is defined in a YAML file — no web UI for configuration.

What you get

• YAML-driven configuration — version-controllable, reviewable, deployable
• Condition-based alerting — flexible expressions like [STATUS] == 200 && [RESPONSE_TIME] < 500
• Status page — clean, auto-generated status dashboard
• DNS, TCP, HTTP, ICMP, SSH monitoring
• Alerting — Slack, PagerDuty, Discord, Telegram, email, and more
• External endpoint support — validate response bodies, headers, and certificates

Setup

Create a config.yaml:

endpoints:
  - name: Website
    url: "https://example.com"
    interval: 60s
    conditions:
      - "[STATUS] == 200"
      - "[RESPONSE_TIME] < 1000"
    alerts:
      - type: slack
        send-on-resolved: true

  - name: API Health
    url: "https://api.example.com/health"
    interval: 30s
    conditions:
      - "[STATUS] == 200"
      - "[BODY].status == UP"

Run with Docker:

docker run -d \
  --name gatus \
  -p 8080:8080 \
  -v ./config.yaml:/config/config.yaml \
  twinproduction/gatus

Strengths

• Configuration as code — Store your monitoring config in Git alongside your infrastructure
• Lightweight — Written in Go, uses minimal resources (~30 MB RAM)
• Flexible conditions — JSONPath expressions for response body validation
• Clean status page — Minimal, fast, auto-generated from your config
• Easy to integrate into CI/CD — just update the YAML and redeploy

Weaknesses

• No web-based configuration — you must edit YAML files
• No incident management — status page shows up/down but no manual incident notes
• Smaller community — Less battle-tested than Uptime Kuma
• No maintenance windows — scheduled downtime will trigger alerts
• Limited historical data — default in-memory storage (PostgreSQL optional for persistence)

Resource requirements

• Minimum: 64 MB RAM, 1 vCPU
• Recommended: 128 MB RAM for 100+ endpoints
• Storage: Minimal with in-memory; varies with PostgreSQL

Statping-ng

The middle ground. Statping-ng (a maintained fork of the original Statping) offers a web UI like Uptime Kuma with some of the developer-friendly features of Gatus.

What you get

• Web dashboard for monitoring and configuration
• Public status page with custom branding
• HTTP, TCP, ICMP, gRPC monitoring
• Notification channels — Slack, email, Telegram, webhook, and more
• API-first — Full REST API for automation
• Multiple database backends — SQLite, PostgreSQL, MySQL

Setup

docker run -d \
  --name statping \
  -p 8080:8080 \
  -v statping:/app \
  adamboutcher/statping-ng

Strengths

• API-first design — Everything configurable via REST API
• Flexible database backends — Choose SQLite for simplicity or PostgreSQL for scale
• Theme customization — Custom CSS/HTML for your status page
• gRPC monitoring — Useful if you run gRPC services

Weaknesses

• Less actively maintained — Development has slowed compared to Uptime Kuma and Gatus
• Smaller community — Fewer users means less community support
• UI is functional but dated — Not as polished as Uptime Kuma
• Documentation gaps — Some features aren't well-documented

Resource requirements

• Minimum: 128 MB RAM, 1 vCPU
• Recommended: 256 MB RAM
• Storage: Depends on database backend and retention

Feature Comparison

Which Should You Choose?

Choose Uptime Kuma if:

• You want the best web UI and don't mind clicking to configure
• You need status pages with incident management
• You monitor a mix of different service types
• You value a large community and active development

Choose Gatus if:

• You prefer configuration-as-code and want monitoring config in Git
• You want the lightest possible resource footprint
• You need complex response validation (JSONPath conditions)
• You're already using IaC (Terraform, Ansible) and want monitoring to fit that workflow

Choose Statping-ng if:

• You need API-driven configuration for automation
• You want a web UI but also need programmatic control
• You run gRPC services that need monitoring

Our pick: For most self-hosters, Uptime Kuma is the safe choice. Its UI, community, and feature set are hard to beat. But if you're a developer who manages infrastructure as code, Gatus is elegant and refreshingly simple.

A Note on Architecture

None of these tools support high-availability clustering out of the box. That means if your monitoring server goes down, your monitoring goes with it.

Mitigate this with:

1. Run monitoring on a separate server from what you're monitoring
2. Use a different cloud provider than your main infrastructure
3. Pair with a free external service (UptimeRobot free tier) as a backup
4. Set up a dead man's switch — a service that alerts you if it stops hearing from your monitor

This layered approach gives you resilient monitoring without the complexity of running clustered monitoring infrastructure.